4 th Annual Healthcare Informatics Symposium April 29 th , 2011 Richard ‘RJ’ Kedziora

1. Design and Implementation of a Diabetes Medication Computer Assisted Decision Support (CADS) System 4th Annual Healthcare Informatics Symposium April 29th, 2011 Richard ‘RJ’ Kedziora

2. Funding / Disclosures • Founding Partner/Owner - Estenda Solutions • Funding from • U.S. Army Medical Research and Materiel Command (USARMC) AMEDD Advanced Medical Technology Initiative (AAMTI) program. • Congressionally Directed Medical Research Programs administered by Air Force in partnership with University of Pittsburgh Medical Center - sponsored by the honorable U.S. Representative John P. Murtha • PI on grant COL. Robert Vigersky M.D. at Walter Reed Army Medical Center

3. The Problem • Not enough endocrinologist to treat patients with diabetes – most care is managed by primary care doctors • Most patient’s not goal (A1C 6.5 – 7%) • SMBG Issues • SMBG not used effectively by patients / providers • SMBG perception is that it is not used to adjust medications • Large number of drug and combinations • Therapy is not adjusted frequently enough

4. Potential Medication Combinations • Drug classes include: biguanide, DPP-4 inhibitor, GLP-1 agonist, secretagogue, TZD, AGI, and basal insulin • Excluded: Colesevelam and Bromocriptine • 68 potential treatment combinations • 8 mono • 26 dual • 31 triple • 3 quadruple

5. The Solution - CADS • Designed for primary care doctors to assist in better decision-making in modifying patient’s drug regimen to bring their blood glucose into better control. • Currently Type 2 (Type 1 planning) • Idea, concept and rules developed by • COL. Robert Vigersky, M.D. - Director, Diabetes Institute, Endocrinology Service, Department of Medicine, Walter Reed Army Health Care System, Washington DC • David Rodbard, M.D. – Biomedical Informatics Consultants, LLC, Potomac, Maryland

6. Journey • Multiple facilitated group clinical chart reviews to reach consensus • Initial standalone prototype development using CLIPS and Microsoft ASP – early 2000s • Experimented with DROOLS moved to table-driven algorithm coded in Java • Production system coding and integration with CDMP 2009-2010 • Clinical Trial 2011 – 2012 and beyond • FDA Validation

7. Input • Age, Gender, Type of Diabetes • Self-managed blood glucose data (SMBG) • Current and past medications • Adverse Reactions • Labs (A1C, ALT, Creatinine) • Significant Diagnoses • Renal, Hepatic, Gastrointestinal, Cardiac • Target A1C

11. SMBG Testing Protocol • For 3 months • Twice daily (or more depending on DR. discretion) • Once a week • Before meals (x3) and bedtime = 4 tests • Once a month • Before and 2 hours after meals (x3), bedtime and night at approximately 3AM = 8 tests

12. Pre-Analysis • Availability of SMBG • SMBG correlation with most recent A1c • Identification of problem time-frames based on SMBG data • Hypoglycemia • Hyperglycemia • Variability

13. Analysis • Overall quality of glycemic control • Effectiveness of SMBG testing • Inappropriate medication combinations • Existing Medication Contraindications • Age, Gender, Labs, Diagnoses • Based on SMBG profile analysis and medication effectiveness • First, address Hypoglycemia • Then address Hyperglycemia

14. Algorithm for Treatment of Type 2 Diabetes Diet and Exercise If A1C > 6.5% Monotherapy or Combination Therapy Not adequate Adequate Follow-up q 3 mo Other Oral Combinations Not adequate Adequate Follow-up q 3 mo Oral Agent Plus Insulin at Bedtime (Glargine or NPH) Not adequate Adequate Split-Mixed Insulin or Lispro or Aspartqac + Glargine or NPH qhs Follow-up q 3 mo

15. Recommendations • Modify the existing regimen because of contraindications • Increase or decrease the dosage of current medication(s) • Add additional oral agents/basal insulin • 5+ medications or 4 with hyperglycemia - recommendation to consult endocrinologist

16. Additional Output • Where testing can be improved • FDA Warnings • Rosiglitazone use has been severely restricted by the FDA because of concerns that it causes an increased number of cardiovascular events. Continued use requires your patient be enrolled in a risk evaluation and mitigation strategy program established by GlaxoSmithKline. You should consider switching this patient to pioglitazone at an equivalent dose… • SMBG Profile by Time Period • Min, Max, Average, Standard Deviation • % high, % low based on thresholds

23. Algorithm Development • Started with Expert Rules System • Initially used CLIPS • Migrated to DROOLS • Final solution - Table-driven logic with algorithm coded in Java • Number of combinations • Ability for versioning and customization by individual non-rule experts

25. Next Steps • A one-year multi-site IRB-approved, cluster-randomized controlled trial • Expand rule base to include • Insulin dependant Type 2 • Type 1 • Expanded pattern recognition and treatment plans • Post-prandial fluctuations • Trends during day or night • Hypoglycemia followed by rebound "Somogyi reaction“ • Dawn Phenomenon

26. Publication • Rodbard and Vigersky, Design of a Decision Support System to Help Clinicians Manage Glycemia in Patients with Type 2 Diabetes • Journal of Diabetes Science and Technology, • Volume 5, Issue 2, March 2011

27. Thank you! Richard ‘RJ’ Kedziora rkedziora@estenda.com Office: (610) 834-2908 Cell: (610) 772-3989

28. CDMP Background • Complete customizable web-based clinical application for management of patients with chronic disease. • Based on the Chronic Care Model, it was originally designed for military healthcare to better manage patients with diabetes. • Evolved into a generalized chronic disease and population health management system supporting the Patient Centered Medical Home model.  For details visit: http://cdmp.estenda.com